Flow, turbulence, and pollutant dispersion in urban atmospheres

背景(考古学) 湍流 城市新陈代谢 人口 大气(单位) 气象学 环境科学 物理 城市规划 地理 城市密度 生态学 生物 社会学 人口学 考古
作者
Harindra J. S. Fernando,Dragan Zajic,Silvana Di Sabatino,Reneta Dimitrova,Brent C. Hedquist,Ann Dallman
出处
期刊:Physics of Fluids [American Institute of Physics]
卷期号:22 (5) 被引量:151
标识
DOI:10.1063/1.3407662
摘要

The past half century has seen an unprecedented growth of the world’s urban population. While urban areas proffer the highest quality of life, they also inflict environmental degradation that pervades a multitude of space-time scales. In the atmospheric context, stressors of human (anthropogenic) origin are mainly imparted on the lower urban atmosphere and communicated to regional, global, and smaller scales via transport and turbulence processes. Conversely, changes in all scales are transmitted to urban regions through the atmosphere. The fluid dynamics of the urban atmospheric boundary layer and its prediction is the theme of this overview paper, where it is advocated that decision and policymaking in urban atmospheric management must be based on integrated models that incorporate cumulative effects of anthropogenic forcing, atmospheric dynamics, and social implications (e.g., health outcomes). An integrated modeling system juxtaposes a suite of submodels, each covering a particular range of scales while communicating with models of neighboring scales. Unresolved scales of these models need to be parametrized based on flow physics, for which developments in fluid dynamics play an indispensible role. Illustrations of how controlled laboratory, outdoor (field), and numerical experiments can be used to understand and parametrize urban atmospheric processes are presented, and the utility of predictive models is exemplified. Field experiments in real urban areas are central to urban atmospheric research, as validation of predictive models requires data that encapsulate four-dimensional complexities of nature.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
牛豁完成签到,获得积分10
刚刚
研友_惊鸿发布了新的文献求助10
1秒前
3秒前
3秒前
2021完成签到 ,获得积分0
3秒前
叶子完成签到,获得积分10
3秒前
漂亮的千万完成签到,获得积分10
4秒前
6秒前
FashionBoy应助vc采纳,获得10
6秒前
7秒前
bkagyin应助科研科研采纳,获得10
8秒前
颜苏YANSU发布了新的文献求助10
8秒前
科研通AI6.4应助梓翔采纳,获得10
8秒前
Lucas应助梓翔采纳,获得10
8秒前
舒心的飞荷完成签到 ,获得积分10
9秒前
飞快的千万应助认真听露采纳,获得10
9秒前
10秒前
木炎发布了新的文献求助10
11秒前
14秒前
学术孤儿应助研友_惊鸿采纳,获得30
14秒前
妮妮妮完成签到 ,获得积分10
14秒前
科研小菜鸟完成签到,获得积分10
15秒前
15秒前
沐啊完成签到 ,获得积分10
18秒前
chenxi发布了新的文献求助10
20秒前
20秒前
科研科研发布了新的文献求助10
21秒前
激动的以寒完成签到,获得积分10
21秒前
22秒前
23秒前
虚心白凡完成签到,获得积分10
24秒前
25秒前
科研通AI6.2应助Timezzz采纳,获得30
25秒前
26秒前
27秒前
aaaamy发布了新的文献求助10
28秒前
科研通AI6.2应助ADP采纳,获得10
28秒前
深情安青应助木炎采纳,获得10
29秒前
zjy发布了新的文献求助10
29秒前
颜苏YANSU完成签到,获得积分10
29秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Health Psychology 600
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 500
Variations: A More Diverse Picture of Contemporary Art 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7590932
求助须知:如何正确求助?哪些是违规求助? 9168321
关于积分的说明 19624315
捐赠科研通 7169767
什么是DOI,文献DOI怎么找? 3267407
关于科研通互助平台的介绍 2432229
邀请新用户注册赠送积分活动 2259689